How to Use the Image Router MCP in AutoGen

Q: How do AutoGen agents negotiate model selection using Image Router?

One agent queries listmodels to check capabilities, while another evaluates the budget. They compare notes and pass the selected model ID using this MCP Server's tools to execute the generation.

Let your AutoGen agents debate and agree on the best image generation engine before spending a single API credit.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Image Router MCP to AutoGen

Create your Vinkius account to connect Image Router to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Image Router with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Consensus-driven image routing in AutoGen

AutoGen excels when agents argue over the best path forward. By giving your design agent and budget agent access to this MCP Server, they can negotiate which model to use. The design agent might call `list_models` to find a high-fidelity engine, while the budget agent checks `list_models_by_category` to find a cheaper alternative. Once they reach a consensus, the executing agent triggers `generate_image` or `generate_image_advanced` based on their agreed parameters. This collaborative decision loop prevents runaway costs while keeping image quality high.

Multi-agent verification and upscaling

You can set up a dedicated quality assurance agent in AutoGen using this MCP Server. After the creator agent runs `generate_image`, the QA agent polls `get_generation_status` to fetch the final output. If the resolution is too low, the QA agent instructs the upscaler agent to call `upscale_image`. If the image composition is off, the QA agent can trigger `edit_image` with specific correction instructions. This multi-agent feedback loop ensures that only approved visual assets make it to your final delivery folder.

Automated variation testing and status checks

Testing multiple creative directions is easy when agents handle the work. An editor agent can take a base image and call `generate_variation` across different styles fetched from `list_styles`. This allows your team to review dozens of options without manually configuring each run. Before launching a large batch, a coordinator agent runs `check_imagerouter_status` to make sure all external APIs are responsive. This prevents your agent conversation from stalling due to silent upstream failures.

Setup guide

Set up Image Router MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Image Router tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Image Router_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Image Router data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Image Router_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Image Router data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Image Router. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Image Router now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Image Router MCP in AutoGen

One agent queries `list_models` to check capabilities, while another evaluates the budget. They compare notes and pass the selected model ID using this MCP Server's tools to execute the generation.

Yes, your AutoGen group chat can dispatch multiple agents to call `generate_image_advanced` concurrently. The server handles the concurrent connections and routes each request to its respective backend.

An agent can analyze an existing image, write a modification prompt, and call `edit_image` directly via the MCP Server. The agent can then call `get_generation_status` to verify the edits were applied correctly.

The execution will fail, but your agent can catch the error, call `check_imagerouter_status` to verify connectivity, and select an alternative model from `list_models`.

All prompt strings and image binaries are processed in secure, ephemeral V8 isolates. No data is written to disk or used for training, ensuring complete confidentiality for your creative assets.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript